• Filter By:
• Specialty
• Source
• Condition
• Cancer
• Infectious Disease
• Drug
• Training
• Management
• Procedure
• Therapy
• Vaccine
• Nutrition
• Country

CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study
This study aimed to develop a machine learning model for predicting brain arteriovenous malformation (bAVM) rupture using a combination of traditional risk factors and radiomics features. This multicenter retrospective study enrolled 586 patients with unruptured bAVMs from 2010 to 2020. All patients were grouped into the hemorrhage (n = 368) and non-hemorrhage (n = 218) groups. The bAVM nidus were segmented on CT angiography images using Slicer software, and radiomic features were extracted using Pyradiomics. The dataset included a training set and an independent testing set. The machine learning model was developed on the...
Source: Translational Stroke Research - June 13, 2023 Category: Neurology Source Type: research

Alpha-Asarone Ameliorates Neurological Dysfunction of Subarachnoid Hemorrhagic Rats in Both Acute and Recovery Phases via Regulating the CaMKII-Dependent Pathways
AbstractEarly brain injury (EBI) is the leading cause of poor prognosis for patients suffering from subarachnoid hemorrhage (SAH), particularly learning and memory deficits in the repair phase. A recent report has involved calcium/calmodulin-dependent protein kinase II (CaMKII) in the pathophysiological process underlying SAH-induced EBI. Alpha-asarone (ASA), a major compound isolated from the Chinese medicinal herbAcorus tatarinowii Schott, was proven to reduce secondary brain injury by decreasing CaMKII over-phosphorylation in rats ’ model of intracerebral hemorrhage in our previous report. However, the effect of ASA o...
Source: Translational Stroke Research - February 13, 2023 Category: Neurology Source Type: research

The Woven EndoBridge (WEB) Device for the Treatment of Intracranial Aneurysms: Ten Years of Lessons Learned and Adjustments in Practice from the WorldWideWEB Consortium
This study comprised 671 patients (median age 61.4 years; 71.2% female) with 682 intracranial aneurysms. Over time, we observed an increasing tendency to treat patients presenting with ruptured aneurysms and aneurysms with smaller neck, diameter, and dome widths. Furthermore, we observed a trend towards more off-label use of the WEB for sidewall aneurysms and increased adoption of transradial access for WEB deployment. Moreover, the proportion of patients with adequate WEB o cclusion immediately and at last follow-up was significantly higher in more recent year cohorts, as well as lower rates of compaction and retreatment...
Source: Translational Stroke Research - September 6, 2022 Category: Neurology Source Type: research

Collateral-Core Ratio as a Novel Predictor of Clinical Outcomes in Acute Ischemic Stroke
AbstractThe interaction effect between collateral circulation and ischemic core size on stroke outcomes has been highlighted in acute ischemic stroke (AIS). However, biomarkers that assess the magnitude of this interaction are still lacking. We aimed to present a new imaging marker, the collateral-core ratio (CCR), to quantify the interaction effect between these factors and evaluate its ability to predict functional outcomes using machine learning (ML) in AIS. Patients with AIS caused by anterior circulation large vessel occlusion (LVO) were recruited from a prospective multicenter study. CCR was calculated as collateral ...
Source: Translational Stroke Research - July 25, 2022 Category: Neurology Source Type: research

Circulating Plasma miRNA Homologs in Mice and Humans Reflect Familial Cerebral Cavernous Malformation Disease
AbstractPatients with familial cerebral cavernous malformation (CCM) inherit germline loss of function mutations and are susceptible to progressive development of brain lesions and neurological sequelae during their lifetime. To date, no homologous circulating molecules have been identified that can reflect the presence of germ line pathogenetic CCM mutations, either in animal models or patients. We hypothesize that homologous differentially expressed (DE) plasma miRNAs can reflect the CCM germline mutation in preclinical murine models and patients. Herein, homologous DE plasma miRNAs with mechanistic putative gene targets...
Source: Translational Stroke Research - June 17, 2022 Category: Neurology Source Type: research

Machine Learning –Based Identification of Target Groups for Thrombectomy in Acute Stroke
AbstractWhether endovascular thrombectomy (EVT) improves functional outcome in patients with large-vessel occlusion (LVO) stroke that do not comply with inclusion criteria of randomized controlled trials (RCTs) but that are considered for EVT in clinical practice is uncertain. We aimed to systematically identify patients with LVO stroke underrepresented in RCTs who might benefit from EVT. Following the premises that (i) patients without reperfusion after EVT represent a non-treated control group and (ii) the level of reperfusion affects outcome in patients with benefit from EVT but not in patients without treatment benefit...
Source: Translational Stroke Research - June 7, 2022 Category: Neurology Source Type: research

A Deep Learning-Based Automatic Collateral Assessment in Patients with Acute Ischemic Stroke
This study aimed to develop a supervised deep learning (DL) model for grading collateral status from dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) images from patients with large vessel occlusion (LVO) acute ischemic stroke (AIS) and compare its performance against experts ’ manual grading. Among consecutive LVO-AIS at three medical center sites, DSC-MRP data were processed to generate collateral flow maps consisting of arterial, capillary, and venous phases. With the use of expert readings as a reference, a DL model was developed to analyze collateral status with o utput classified into good and...
Source: Translational Stroke Research - May 21, 2022 Category: Neurology Source Type: research

Subarachnoid Hemorrhage Induces Sub-acute and Early Chronic Impairment in Learning and Memory in Mice
AbstractSubarachnoid hemorrhage (SAH) leads to significant long-term cognitive deficits, so-called the post-SAH syndrome. Existing neurological scales used to assess outcomes of SAH are focused on sensory-motor functions. To better evaluate short-term and chronic consequences of SAH, we explored and validated a battery of neurobehavioral tests to gauge the functional outcomes in mice after the circle of Willis perforation-induced SAH. The 18-point Garcia scale, applied up to 4  days, detected impairment only at 24-h time point and showed no significant difference between the Sham and SAH group. A decrease in locomotion wa...
Source: Translational Stroke Research - March 8, 2022 Category: Neurology Source Type: research

Disruption of Sonic Hedgehog Signaling Accelerates Age-Related Neurogenesis Decline and Abolishes Stroke-Induced Neurogenesis and Leads to Increased Anxiety Behavior in Stroke Mice
In this study, using conditional knockout (cKO) of SHH signaling receptorSmo gene in NSCs, we show a decreased neurogenesis at both SVZ and SGZ in young-adult mice and an accelerated depletion of neurogenic cells in the process of aging suggesting that SHH signaling is critical in maintaining neurogenesis during aging. Behavior studies revealed that compromised neurogenesis inSmo cKO mice leads to increased anxiety/depression-like behaviors without affecting general locomotor function or spatial and fear-related learning. Importantly, we also show that NSCs with a  cKO of SHH signaling abolishes stroke-induced neurogenesi...
Source: Translational Stroke Research - February 11, 2022 Category: Neurology Source Type: research

U-net Models Based on Computed Tomography Perfusion Predict Tissue Outcome in Patients with Different Reperfusion Patterns
AbstractEvaluation of cerebral perfusion is important for treatment selection in patients with acute large vessel occlusion (LVO). To assess ischemic core and tissue at risk more accurately, we developed a deep learning model named U-net using computed tomography perfusion (CTP) images. A total of 110 acute ischemic stroke patients undergoing endovascular treatment with major reperfusion ( ≥ 80%) or minimal reperfusion (≤ 20%) were included. Using baseline CTP, we developed two U-net models: one model in major reperfusion group to identify infarct core; the other in minimal reperfusion group to identify tissue at r...
Source: Translational Stroke Research - January 19, 2022 Category: Neurology Source Type: research

Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)
AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), suba...
Source: Translational Stroke Research - August 14, 2021 Category: Neurology Source Type: research

Machine Learning-Enabled Determination of Diffuseness of Brain Arteriovenous Malformations from Magnetic Resonance Angiography
AbstractThe diffuseness of brain arteriovenous malformations (bAVMs) is a significant factor in surgical outcome evaluation and hemorrhagic risk prediction. However, there are still predicaments in identifying diffuseness, such as the judging variety resulting from different experience and difficulties in quantification. The purpose of this study was to develop a machine learning (ML) model to automatically identify the diffuseness of bAVM niduses using three-dimensional (3D) time-of-flight magnetic resonance angiography (TOF-MRA) images. A total of 635 patients with bAVMs who underwent TOF-MRA imaging were enrolled. Three...
Source: Translational Stroke Research - August 12, 2021 Category: Neurology Source Type: research

Ifenprodil Improves Long-Term Neurologic Deficits Through Antagonizing Glutamate-Induced Excitotoxicity After Experimental Subarachnoid Hemorrhage
AbstractExcessive glutamate leading to excitotoxicity worsens brain damage after SAH and contributes to long-term neurological deficits. The drug ifenprodil is a non-competitive antagonist of GluN1-GluN2B N-methyl-d-aspartate (NMDA) receptor, which mediates excitotoxic damage in vitro and in vivo. Here, we show that cerebrospinal fluid (CSF) glutamate level within 48  h was significantly elevated in aSAH patients who later developed poor outcome. In rat SAH model, ifenprodil can improve long-term sensorimotor and spatial learning deficits. Ifenprodil attenuates experimental SAH-induced neuronal death of basal cortex and h...
Source: Translational Stroke Research - March 12, 2021 Category: Neurology Source Type: research

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage
AbstractWe hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional ...
Source: Translational Stroke Research - February 6, 2021 Category: Neurology Source Type: research

Stability Assessment of Intracranial Aneurysms Using Machine Learning Based on Clinical and Morphological Features
AbstractMachine learning (ML) as a novel approach could help clinicians address the challenge of accurate stability assessment of unruptured intracranial aneurysms (IAs). We developed multiple ML models for IA stability assessment and compare their performances. We enrolled 1897 consecutive patients with unstable (n = 528) and stable (n = 1539) IAs. Thirteen patient-specific clinical features and eighteen aneurysm morphological features were extracted to generate support vector machine (SVM), random forest (RF), and feed-forward artificial neural network (ANN) models. The discriminatory performances of the models w...
Source: Translational Stroke Research - October 20, 2020 Category: Neurology Source Type: research

Pages: 1  2